pgm_index 0.3.2

Ultra-fast learned PGM-Index for efficient sorted key lookup with bounded error
Documentation

๐Ÿ“ pgm_index โ€” Learned Index for Sorted Keys

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PGM-Index is a space-efficient data structure for fast lookup in sorted sequences.
It approximates the distribution of keys with piecewise linear models, allowing searches in O(log ฮต) with a guaranteed error bound.

๐Ÿ“„ Algorithm

Based on the work by Paolo Ferragina & Giorgio Vinciguerra:

The PGM-index: a fully-dynamic compressed learned index with provable worst-case bounds (2020)
๐Ÿ”— Paper ยท ๐ŸŒ Official site

๐Ÿ“ฆ Installation

Add to your Cargo.toml:

[dependencies]
pgm_index = "*"

๐Ÿ›  Usage

use pgm_index::PGMIndex;

fn main() {
    let data: Vec<u64> = (0..1_000_000).collect();
    let pgm = PGMIndex::new(&data, 32); // ฮต = 32

    let key = 123456;
    if let Some(pos) = pgm.search(key) {
        println!("Found at position {}", pos);
    } else {
        println!("Not found");
    }
}

๐ŸŽ Benchmarks by ฮต

Dataset: 1,000,000 elements, 100,000 random queries CPU: Intel Core i7 12700, Windows 11, single-threaded

ฮต Build Time Mem Usage Segments Single Lookup Batch Lookup Avg ns/query
16 2.19 ms 7.99 MB 3906 20.84 M/s 25.35 M/s 48.0 / 39.4
32 2.22 ms 7.87 MB 1953 24.09 M/s 24.98 M/s 41.5 / 40.0
64 2.05 ms 7.75 MB 976 21.96 M/s 26.06 M/s 45.5 / 38.4
128 2.07 ms 7.69 MB 488 19.64 M/s 25.56 M/s 50.9 / 39.1

Binary Search (baseline): 4.32 ms, 23.13 M/s, 43.2 ns/query.

๐Ÿ“Š Comparison to Other Indexes (1M elements) *

Using ฮต = 32 as a balanced configuration:

Structure Memory Usage Build Time Lookup Speed (single) Batch Lookup Speed
PGM-Index (ฮต=32) 7.87 MB 2.22 ms 24.09 M/s 24.98 M/s
Binary Search ~8.0 MB โ€” (no build) 23.13 M/s (0.96ร—) 23.13 M/s (0.93ร—)
BTreeMap ~24 MB ~50 ms ~4.0 M/s (0.17ร—) ~4.0 M/s (0.16ร—)
HashMap ~64 MB ~15 ms ~40.0 M/s (1.66ร—) ~40.0 M/s (1.60ร—)
  • our benchmarks

๐Ÿ“ˆ Relative Performance *

Metric vs Binary Search vs BTreeMap vs HashMap
Memory 1.02ร— better 3.05ร— better 8.13ร— better
Build Time โ€” 22.5ร— faster 6.8ร— faster
Single Lookup 1.04ร— faster 6.0ร— faster 0.6ร— slower
Batch Lookup 1.08ร— faster 6.2ร— faster 0.62ร— slower
  • our benchmarks

๐Ÿ“Œ Potential Use Cases

  • Indexing large sorted numeric datasets
  • Time-series databases
  • Read-optimized storage engines
  • Scientific & bioinformatics data search
  • Columnar store secondary indexes

๐Ÿ“œ License

MIT